Manufacturing Analysis

Dataset Link

manufacturing_data.csv

📌 Objective

The purpose of this dataset is to analyze the end-to-end manufacturing process for various products, evaluating factors such as cost, time efficiency, supplier performance, and profitability to inform strategic supply chain decisions.

 

📊 Dataset Fields

Column Name

Data Type

Description

Product type

String

Category of the product, e.g., Haircare, Skincare.

SKU

String

Unique Stock Keeping Unit for each product.

Price

Float

Selling price per unit of the product.

Availability

Integer

Number of units available at the store or warehouse.

Number of products sold

Integer

Total units sold during the observed period.

Revenue generated

Float

Total revenue = Price × Units Sold.

Customer demographics

String

Demographic information about buyers (Male, Female, Non-binary, Unknown).

Stock levels

Integer

Current inventory levels.

Lead times

Integer

Days taken from order to arrival of goods.

Order quantities

Integer

Units ordered from supplier.

Shipping times

Integer

Time taken for product shipment (days).

Shipping carriers

String

Name of shipping carrier (e.g., Carrier A, B).

Shipping costs

Float

Shipping cost per order/unit.

Supplier name

String

Name of the supplier.

Location

String

Geographic location of the supplier.

Lead time

Integer

Supplier-specific delivery lead time.

Production volumes

Integer

Total units manufactured in the cycle.

Manufacturing lead time

Integer

Days required to manufacture the product.

Manufacturing costs

Float

Cost to manufacture one unit of the product.

Inspection results

String

Result of quality inspection (Pending, Pass, Fail).

Defect rates

Float (0-1)

Proportion of defective items in manufacturing.

Transportation modes

String

Mode of transport used (e.g., Road, Air).

Routes

String

Route identifier used for logistics.

Costs

Float

Transportation cost associated with the route.

 

🔢 Key Calculated Metrics

  1. Total Lead Time
    Lead time + Manufacturing lead time
    ➤ Represents end-to-end time taken from order to availability.
     
  2. Profit per Unit
    Price - Manufacturing costs
    ➤ Reflects direct unit-level profit before logistics, marketing, etc.
     
  3. Profit Margin (%)
    (Profit per Unit / Price) × 100
    ➤ Indicates pricing efficiency relative to manufacturing cost.
     
  4. Efficiency Score
    Production volumes / Total Lead Time
    ➤ Measures productivity; higher values indicate more efficient production.
     

 

📈 Use Cases

  • Identifying inefficient SKUs and suppliers.
     
  • Improving cost margins through optimal shipping or supplier choice.
     
  • Predicting stockout risks by correlating lead times and demand.
     
  • Prioritizing high-efficiency products for promotional campaigns.

 

Google Collab: Manufacturing Analysis